We develop a model to determine an optimal investment strategy to improve the performance of undergraduate students in the US. Our model has three parts: In the first part, we collect data about the focus of other foundations’ investment by subjects and locations. We consider the charitable identity of the Goodgrant as well. Then we set out to decide our focus, which is to invest more on those schools with more minority races, lower educational performance, higher debt ratio and so on. In this part, we also classify the data into two groups, one for school selecting, and another for ROI determining. In the second part, as a data extraction, we build an efficient and intuitive model to rank the candidate schools in accordance with the correlation of our focus, using the PCA method. After that, the top 50 schools are selected as our target schools. In the third part, we make a key assumption that the social utility of a school has logarithmic relationship with the earnings of graduated students and the graduation rate. More over, we create a parameter k to denote the marginal rate of substitution (MRS) between the two factors above. After that, we come to define the ROI function of each target school as the incremental utility. We further discuss how to devise the best strategy with several methods. At last, we choose the improved PSO algorithm based on augmented Lagrange function. This algorithm is a typical method to solve the multivariable optimization problem with constraint conditions. Then we offer a recommending list by the cumulative ROI in five years. What’s more, our model is broad enough to accommodate any non-linear constraint optimization problem. Finally, we change the numerical value of parameter k to examine the sensitivity of our investment strategy. The result shows that our model is robust.
| Published in | International Journal of Statistical Distributions and Applications (Volume 2, Issue 4) |
| DOI | 10.11648/j.ijsd.20160204.13 |
| Page(s) | 54-66 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2016. Published by Science Publishing Group |
Principal Component Analysis, Big Data, Utility Function, Lagrange Multiplier, Karush–Kuhn–Tucker Conditions, Particle Swarm Optimization
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APA Style
Li Yizhang, Zhao Xinyu, Chen Meng. (2016). The Optimal Investment Strategy Based on the Large-Scale Non-linear Constraint Optimization Methods. International Journal of Statistical Distributions and Applications, 2(4), 54-66. https://doi.org/10.11648/j.ijsd.20160204.13
ACS Style
Li Yizhang; Zhao Xinyu; Chen Meng. The Optimal Investment Strategy Based on the Large-Scale Non-linear Constraint Optimization Methods. Int. J. Stat. Distrib. Appl. 2016, 2(4), 54-66. doi: 10.11648/j.ijsd.20160204.13
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Download@article{10.11648/j.ijsd.20160204.13,
author = {Li Yizhang and Zhao Xinyu and Chen Meng},
title = {The Optimal Investment Strategy Based on the Large-Scale Non-linear Constraint Optimization Methods},
journal = {International Journal of Statistical Distributions and Applications},
volume = {2},
number = {4},
pages = {54-66},
doi = {10.11648/j.ijsd.20160204.13},
url = {https://doi.org/10.11648/j.ijsd.20160204.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20160204.13},
abstract = {We develop a model to determine an optimal investment strategy to improve the performance of undergraduate students in the US. Our model has three parts: In the first part, we collect data about the focus of other foundations’ investment by subjects and locations. We consider the charitable identity of the Goodgrant as well. Then we set out to decide our focus, which is to invest more on those schools with more minority races, lower educational performance, higher debt ratio and so on. In this part, we also classify the data into two groups, one for school selecting, and another for ROI determining. In the second part, as a data extraction, we build an efficient and intuitive model to rank the candidate schools in accordance with the correlation of our focus, using the PCA method. After that, the top 50 schools are selected as our target schools. In the third part, we make a key assumption that the social utility of a school has logarithmic relationship with the earnings of graduated students and the graduation rate. More over, we create a parameter k to denote the marginal rate of substitution (MRS) between the two factors above. After that, we come to define the ROI function of each target school as the incremental utility. We further discuss how to devise the best strategy with several methods. At last, we choose the improved PSO algorithm based on augmented Lagrange function. This algorithm is a typical method to solve the multivariable optimization problem with constraint conditions. Then we offer a recommending list by the cumulative ROI in five years. What’s more, our model is broad enough to accommodate any non-linear constraint optimization problem. Finally, we change the numerical value of parameter k to examine the sensitivity of our investment strategy. The result shows that our model is robust.},
year = {2016}
}
TY - JOUR T1 - The Optimal Investment Strategy Based on the Large-Scale Non-linear Constraint Optimization Methods AU - Li Yizhang AU - Zhao Xinyu AU - Chen Meng Y1 - 2016/12/29 PY - 2016 N1 - https://doi.org/10.11648/j.ijsd.20160204.13 DO - 10.11648/j.ijsd.20160204.13 T2 - International Journal of Statistical Distributions and Applications JF - International Journal of Statistical Distributions and Applications JO - International Journal of Statistical Distributions and Applications SP - 54 EP - 66 PB - Science Publishing Group SN - 2472-3509 UR - https://doi.org/10.11648/j.ijsd.20160204.13 AB - We develop a model to determine an optimal investment strategy to improve the performance of undergraduate students in the US. Our model has three parts: In the first part, we collect data about the focus of other foundations’ investment by subjects and locations. We consider the charitable identity of the Goodgrant as well. Then we set out to decide our focus, which is to invest more on those schools with more minority races, lower educational performance, higher debt ratio and so on. In this part, we also classify the data into two groups, one for school selecting, and another for ROI determining. In the second part, as a data extraction, we build an efficient and intuitive model to rank the candidate schools in accordance with the correlation of our focus, using the PCA method. After that, the top 50 schools are selected as our target schools. In the third part, we make a key assumption that the social utility of a school has logarithmic relationship with the earnings of graduated students and the graduation rate. More over, we create a parameter k to denote the marginal rate of substitution (MRS) between the two factors above. After that, we come to define the ROI function of each target school as the incremental utility. We further discuss how to devise the best strategy with several methods. At last, we choose the improved PSO algorithm based on augmented Lagrange function. This algorithm is a typical method to solve the multivariable optimization problem with constraint conditions. Then we offer a recommending list by the cumulative ROI in five years. What’s more, our model is broad enough to accommodate any non-linear constraint optimization problem. Finally, we change the numerical value of parameter k to examine the sensitivity of our investment strategy. The result shows that our model is robust. VL - 2 IS - 4 ER -
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